wahukariuki/HateSpeech

Hate Speech Detecting Plug-in for Social Media Platforms in English and Swahili Languages # Hate Speech Detecting Browser Plug-In For Social Media Platforms : English and Swahili languages # Inspirations Social Media is the most commonly used way of communication in these recent times. Over the years, the use of Hate Speech has increased in the social media platforms. Hate Speech is the use of hate language towards marginalised groups such as race, gender and religion. This has resulted to an increase in mental health issues. Therefore, the implementation of the Hate Speech Detecting plug-in in different social media platforms will uphold the commitment to fostering positive interactions and ensuring a safe digital space for all users. This will enhance transparency and make everyone feel included. # System Architecture # What it does The Hate Speech Detecting Plug-In is embedded as a chrome extention which can be used by social media users in social media platform, to hide content that contains hate speech and to warn the user against the use of hate speech. # How we built it The plug-in is built using HTML, CSS and javascript. The backend server and the model are written in python and deployed using flask. We utilised tensorflow and nltk for training our sentiment analysis model. # Challenges we ran into 1. Finding datasets to train our model 2. Trying to connect our back-end server to the social media APIs # Accomplishments 1. Learning how to train a model 2. Learning how to create a chrome extension 3. Learning how to integrate the model, back-end server and the chrome extension # What's Next? The Hate Speech Detecting Plug-In can later be integrated in different social media applications to make it easier to detect hate Speech and a voice to text recognition could be added to detect hate speech in voice speech